Functional imaging is a critical tool in neuroscience. For example, calcium imaging methods are used routinely in hundreds of labs, generating large-scale video datasets whose characteristics (cell shapes, signal-to-noise levels, background activity, signal timescales, etc.) can vary widely depending on the imaging modality and the details of the brain region and cell types being imaged. To handle this data, scientists must solve two basic tasks: we need to extract signals from the raw video data with minimal noise, and we need to store (and share) the data. A number of papers have focused on the first task [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}; however, somewhat surprisingly, very little work has focused on the second task.
